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   https://doi.org/10.1109/LCSYS.2023.3279008  

   Chalaki, B; Tadiparthi, V; Mahjoub, Hossein Nourkhiz; D'sa, Jovin; Moradi_Pari, Ehsan; Armijos, Andres_Chavez; Li, Anni; Cassandras, Christos G. (January 2023, IEEE Control Systems Letters)
2. Modeling Commercial Adaptive Cruise Control (ACC) on Multi-Lane Facilities by Incorporating Receiving-Lane-Change Car-Following

   https://doi.org/10.1109/ITSC58415.2024.10919690  

   Yang, Mingyuan; Badhrudeen, Mohamed; Kan, Xingan David; Yagantekin, Kemal; Lu, Xiao-yun (September 2024, IEEE)
3. A Deep Learning Approach for Lane Detection

   https://doi.org/10.1109/ITSC48978.2021.9564965  

   Getahun, Tesfamchael; Karimoddini, Ali; Mudalige, Priyantha (September 2021, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC))

   State-of-the-art lane detection methods use a variety of deep learning techniques for lane feature extraction and prediction, demonstrating better performance than conventional lane detectors. However, deep learning approaches are computationally demanding and often fail to meet real-time requirements of autonomous vehicles. This paper proposes a lane detection method using a light-weight convolutional neural network model as a feature extractor exploiting the potential of deep learning while meeting real-time needs. The developed model is trained with a dataset containing small image patches of dimension 16 × 64 pixels and a non-overlapping sliding window approach is employed to achieve fast inference. Then, the predictions are clustered and fitted with a polynomial to model the lane boundaries. The proposed method was tested on the KITTI and Caltech datasets and demonstrated an acceptable performance. We also integrated the detector into the localization and planning system of our autonomous vehicle and runs at 28 fps in a CPU on image resolution of 768 × 1024 meeting real-time requirements needed for self-driving cars. 

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4. Interactive Bike Lane Planning using Sharing Bikes' Trajectories

   https://doi.org/10.1109/TKDE.2019.2907091  

   He, Tianfu; Bao, Jie; Ruan, Sijie; Li, Ruiyuan; Li, Yanhua; He, Hui; Zheng, Yu (March 2019, IEEE Transactions on Knowledge and Data Engineering)

   Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task to promote the cycling life style, as well-planned bike lanes can reduce traffic congestions and safety risks. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider one or more key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on the large-scale real world bike trajectory data collected from Mobike, a station-less bike sharing system. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. To improve the efficiency of the bike lane planning system for the urban planner, we propose a novel trajectory indexing structure and deploy the system based on a parallel computing framework (Storm) to improve the system’s efficiency. Finally, extensive experiments and case studies are provided to demonstrate the system efficiency and effectiveness. 

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5. Planning Strategies for Lane Reversals in Transportation Networks

   https://doi.org/10.1109/ITSC48978.2021.9564808  

   Wollenstein-Betech, Salomon; Paschalidis, Ioannis Ch.; Cassandras, Christos G. (September 2021, 24th Intelligent Transportation Systems Conference (IEEE ITSC 2021))